Image Modeling: A Mathematical Framework for Segmentation and Object Detection.

Abstract

Decision rules for segmenting scenes and for detecting the presence of distinguished objects in digital images can be based on classical principles of statistical principles of statistical inference if appropriate mathematical image models are developed for observable pictures. The main goal of this research was to devise and analyze alternation image models for digitized FLIR images. The work has been done in close cooperation with the Advanced Modeling Team of the U.S. Army Night Vision and Electro-Optics Laboratory, Ft. Belvoir, Virginia. This report concentrates of hierarchical Markov Random Field models and their application to restoration and segmentation of FLIR images. Keywords: Image processing; Bayesian methods; Infrared images. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 20, 1987
Accession Number
ADA179904

Entities

People

  • Donald E. Mcclure
  • Donald Geman
  • Stuart Geman
  • Ulf Grenander

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Applied Mathematics
  • Artificial Intelligence
  • Complex Systems
  • Computational Science
  • Computers
  • Databases
  • Detection
  • Detectors
  • Electro-Optics
  • Electromagnetic Radiation
  • Image Processing
  • Mathematics
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Statistical Analysis
  • Statistical Inference

Readers

  • Image Processing and Computer Vision.
  • Regression Analysis.
  • Research Science/Academic Research

Technology Areas

  • AI & ML